K
Kaixu Bai
Researcher at East China Normal University
Publications - 70
Citations - 736
Kaixu Bai is an academic researcher from East China Normal University. The author has contributed to research in topics: Environmental science & Computer science. The author has an hindex of 12, co-authored 58 publications receiving 399 citations. Previous affiliations of Kaixu Bai include University of Central Florida.
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Satellite remote sensing of aerosol optical depth: advances, challenges, and perspectives
TL;DR: Aerosol optical depth (AOD) is widely recognized as a critical indicator in understanding atmospheric physics and regional air quality because of its capability for quantifying aerosol loading in t...
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Spatiotemporal trend analysis for fine particulate matter concentrations in China using high-resolution satellite-derived and ground-measured PM2.5 data.
TL;DR: A systematic trend assessment provides a deepened understanding of PM2.5 variations across China in the past few years in association with the newly promoted action plan and offers a brief guideline for relevant policy making in the future.
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Integrating multisensor satellite data merging and image reconstruction in support of machine learning for better water quality management.
TL;DR: This study develops a remote sensing-based multiscale modeling system by integrating multi-sensor satellite data merging and image reconstruction algorithms in support of feature extraction with machine learning leading to automate continuous water quality monitoring in environmentally sensitive regions.
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Multisensor Satellite Image Fusion and Networking for All-Weather Environmental Monitoring
TL;DR: Cross-mission satellite image fusion, networking, and missing value pixel reconstruction for environmental monitoring are described, and their complex integration is illustrated with a case study of Lake Nicaragua that elucidates the state-of-the-art remote sensing technologies for advancing water quality management.
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Statistical bias correction for creating coherent total ozone record from OMI and OMPS observations
TL;DR: In this paper, a modified statistical bias correction method was proposed based on the quantile-quantile adjustment to remove apparent cross-mission TCO biases between the Ozone Monitoring Instrument (OMI) and Ozone Mapping and Profiler Suite (OMPS).